Migrated from eDJGroupInc.com. Author: Barry Murphy. Published: 2013-05-14 11:42:51  Predictive Coding (PC) is a type of Technology-Assisted Review (TAR) that propagates known decisions about a sample of documents (e.g. responsive, privileged) to the rest of the documents in a corpus.  In the litigation context, PC leverages advanced analytics technologies, algorithms, and/or machine learning to augment human legal reviewers’ knowledge.  Considering that review makes up the bulk of total eDiscovery costs, PC is hugely impactful just within that one small niche.  Beyond the Legal Review phase, PC promises to improve information governance (IG) activities through more effective and automated records and information classification, better defensible deletion projects, and the ability to address the real challenge of Big Data – analyzing unstructured content in a fast, efficient way.Use of PC for IG projects – defensible deletion, automated information classification – is not even in the anecdotal stages yet.  There is a ton of interest in how PC can improve or kick-start IG projects, but precious little activity on the ground.  This is not surprising given that PC just for Legal Review is still in the very early stages of adoption.  Because the PC market is still in its early stages, there is a need for objective, unbiased education about how PC works and can be used to make Legal Review more effective, efficient, and high quality.  To meet that need, eDJ has partnered with Karl Shieneman of ReviewLess to offer a 3 CLE hour Boot Camp in various cities.  The Boot Camp, which has received great reviews, focuses on how PC works, how to validate the results of PC, and what the judiciary thinks of PC (there is a Judicial Roundtable featuring a mix of nationally-known and local Judges).In addition to the Boot Camp, we have now created a series of research reports that will give the market a good framework for understanding PC and the solutions available.  The first report – Predictive Coding: What You Need To Know Now – is now live on the eDJ research site.This eDiscoveryJournal research brief, written by Karl Schieneman and Barry Murphy, explores eDJ’s survey results and market research into Predictive Coding and its impact on eDiscovery and Information Governance practices. The brief is aimed at eDiscovery professionals seeking to understand how Predictive Coding works in the review process and what to consider before making decisions on which Predictive Coding solutions to utilize.  Specifically, readers of this report will get:

  • eDJ’s analysis of our February 2013 Predictive Coding Survey
    • Predictive Coding Adoption (and comparison to adoption versus our 2012 survey)
    • Reasons To Not Use Predictive Coding
    • How Users Leverage Predictive Coding When Using It
    • How Users Source Predictive Coding Solutions
    • A Framework for Analyzing Predictive Coding Solutions
      • Defensibility and Transparency
      • User Experience and Workflow Support
      • Platform Support and Architecture
      • Pricing

Future research reports in this series will focus on taking the covers off Predictive Coding and how to validate the results of Predictive Coding.  eDJ’s research reports are available for download by research subscribers or for a la carte purchase at eDJ’s research site.  You can find the report by following this link: Predictive Coding What You Need To Know Now Abstract.Barry Murphy can be reached at barry@eDJGroupInc.com for offline comments or questions. His active research topics include information governance, Predictive Coding, and the impact of social media on eDiscovery. Find Barry at the following events:

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